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Need help with attached 2 questions. Please refer to reference material for week 2 Q2.ppx document which is attached for answering question 2. Chapter 1 – Introduction to Business Stats Chapter 2 – Displaying Descriptive Statistics - Quantitative and qualitative data, stem and leaf displays, scatter plots   Please Submit Your Document on above topics in Microsoft Word  Please refer to reference material for Q2.ppx document which is attached. Topic reference video: https://www.youtube.com/watch?v=TzyiUeXOG44&feature=youtu.be Topic: 1. Answer the following questions based on the readings. Please submit in a word document. 1. Define Analytics.  2. Describe the differences between descriptive, predictive, and prescriptive analytics 3. How do Quantitative and Qualitative data differ? Can you perform analytics on both types? 4. Define the 3 stages (and 6 steps) of quantitative Analysis. Provide a brief summary of each stage and step. 5. Do you consider yourself analytical, why or why not? If you do not consider yourself analytical, what other strengths do you have that would also support the role of a data analytics? 6. Analytics can answer many questions.  Six key questions are listed in Figure 1-1. Which cell in the 2x3 table describes the type of questions that generally interest you most and why? Is there a question in your current (or past) company/organization that would fall within one of these categories? You can also use the university as an example of an organization. What is the question you would like to answer? 7. Analytics is not always the best approach to make a decision or solve a problem. Please review the examples that describe when analytics are not practical. Do your best to come up with a real world example for each of those five categories. 8. Several types of log and process errors are listed in the table titled ‘Typical Decision -Making Errors’. Which errors do you think should be of greatest concern in relation to a data project. 9. In one of the readings there is an example of a pilot who discussed the analytics used in his cockpit. He said … I still occasionally find it useful to look out the window. What do you think the pilot meant by this, and how could you apply it to your role as a data analyst or decision maker in an organization? 10. Give a brief elevator pitch (written) on why analytics should be used by business? Copyright ©2015 Pearson Education, Inc. 2-* Chapter 2 Copyright ©2015 Pearson Education, Inc. 2-* Chapter 2 Displaying Descriptive Statistics CHAPTER 2 MAP 2.1 The Role Technology Plays in Statistics 2.2 Displaying Quantitative Data 2.3 Displaying Qualitative Data 2.4 Contingency Tables 2.5 Stem and Leaf Display 2.6 Scatter Plots Copyright ©2015 Pearson Education, Inc. 2-* 2.1 The Role Technology Plays in Statistics Microsoft Excel has built-in options for data presentation and statistical analysis You may need to activate Excel’s Analysis Tool Pak Add-in to see these options Copyright ©2015 Pearson Education, Inc. 2-* Statistical Analysis Using Excel 2013 Open Excel 2013, then click on the File tab Click Options shown in the drop down menu. This will open the Excel Options dialog box Select Add-Ins in the left margin… Copyright ©2015 Pearson Education, Inc. 2-* Statistical Analysis Using Excel 2010 Click on Go at the bottom of the screen Select the check boxes for Analysis ToolPak and Analysis ToolPak - VBA in the popup menu and click OK Copyright ©2015 Pearson Education, Inc. 2-* Statistical Analysis Using Excel 2013 Select the Data tab. Click on Data Analysis on the right side of the application bar The Data Analysis pop-up menu should appear in the spreadsheet Copyright ©2015 Pearson Education, Inc. 2-* Installing PHStat PHStat is an Excel Add-in developed by Prentice Hall to provide students with additional features for statistical analysis The software will be referred to throughout the book and is available from the book’s website: www.pearsonhighered.com/donnelly To install PHStat on your Windows PC, follow the instructions on the book’s website Mac users can also find instructions for PHStat on the book’s website Copyright ©2015 Pearson Education, Inc. 2-* 2.2 Displaying Quantitative Data Recall the types of data from Chapter 1: Quantitative Qualitative Types of Data Displaying qualitative data is discussed in section 2.3 Displaying quantitative data is discussed in section 2.2 Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Frequency Distribution A frequency distribution shows the number of data observations that fall into specific intervals Graphically summarize information not readily observable by merely looking at data in a table Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Frequency Distribution Example: Number of iPads sold per day Copyright ©2015 Pearson Education, Inc. 2-* Discrete vs. Continuous Data Discrete data are values based on observations that can be counted and are typically represented by whole numbers represent something that has been counted take on whole numbers such as 0, 1, 2, 3 Continuous data are values that can take on any real numbers, including numbers that contain decimal points usually measured rather than counted Examples are weight, time, and distance Copyright ©2015 Pearson Education, Inc. 2-* Discrete vs. Continuous Data Examples of Discrete data Number of children per family Number of cars listed per insurance policy Vacation days per month Examples of Continuous data Time required to read chapter 2 Thickness of paint applied to a car body Voltage of batteries produced in August Copyright ©2015 Pearson Education, Inc. 2-* Relative Frequency Distributions Relative frequency distributions display the proportion of observations of each class relative to the total number of observations shows the fraction of observations in each class found by dividing each frequency by the total number of observations the fractions in a relative frequency distribution add up to 1.00 Copyright ©2015 Pearson Education, Inc. 2-* Relative Frequency Distributions Two iPads were sold on 28% of the days Example: Copyright ©2015 Pearson Education, Inc. 2-* Cumulative Relative Frequency Distributions A cumulative relative frequency distribution totals the proportion of observations that are less than or equal to the class at which you are looking Shows the accumulated proportion as values vary from low to high Copyright ©2015 Pearson Education, Inc. 2-* Cumulative Relative Frequency Distributions Example: Three iPads or less were sold on 80% of the business days Copyright ©2015 Pearson Education, Inc. 2-* Using a Histogram to Graph a Frequency Distribution A histogram is a graph showing the number of observations in each class of a frequency distribution Excel uses the term “bins” for the classes in the distribution Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Histogram in Excel Select the Data tab, and click on Data Analysis in the upper right corner In the pop-up menu, select Histogram and click OK… 1 1 2 2 Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Histogram in Excel In the Input Range text box, highlight the desired data In the Bin Range text box, highlight the bin values (create bins if not already created before step 1) For Output options, select New Worksheet Ply and Chart Output Click OK 3 4 5 6 Copyright ©2015 Pearson Education, Inc. 2-* Histograms in Excel 7. Customize the Excel graph to make it more attractive 8. Stretch size to better proportion 9. Eliminate “more” bin 10. Modify the graph and axis labels 11. Remove the redundant “Frequency” legend Copyright ©2015 Pearson Education, Inc. 2-* The Shape of Histograms Symmetric the right side is the mirror image of the left side of the distribution Still symmetric, but wider spread Not symmetric Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Frequency Distribution Using Grouped Quantitative Data Ideally, the number of classes in a frequency distribution should be between 4 and 20 Some data sets, particularly those with continuous data, require several values to be grouped together in a single class This grouping prevents having too many classes in the frequency distribution, which can make it difficult to detect patterns Copyright ©2015 Pearson Education, Inc. 2-* Number of Classes One method to determine the number of classes in a frequency distribution is the rule 2k  n where k = Number of classes n = Number of data points Find the lowest value of k that satisfies the rule Suppose n = 50 25 = 32 < 50 (k = 5 is too small) 26 = 64 > 50 (k = 6 is a good choice) Copyright ©2015 Pearson Education, Inc. 2-* Class Width Once k is known, the width of each class can be found The width is the range of numbers to put into each class Round this estimate to a useful whole number that makes the frequency distribution more readable Copyright ©2015 Pearson Education, Inc. 2-* Class Width There is no one correct answer for the class width The goal is to create a histogram to clearly and usefully show the pattern in the data Often there is more than one acceptable way to accomplish this Copyright ©2015 Pearson Education, Inc. 2-* Class Boundaries Class boundaries represent the minimum and maximum values for each class Choose class boundaries that are easy to read   3 to less than 6 minutes 3.21 to less than 6.21 minutes 6 to less than 9 minutes vs. 6.21 to less than 9.21 minutes 9 to less than 12 minutes 9.21 to less than 12.21 minutes Copyright ©2015 Pearson Education, Inc. 2-* Class Frequencies Find class frequencies by counting and recording the number of observations in each class this is easier when the data are sorted Example: Copyright ©2015 Pearson Education, Inc. 2-* Rules for Classes for Grouped Data Equal-size classes. All classes in the frequency distribution must be of equal width Mutually exclusive classes. Class boundaries cannot overlap Include all data values. Make sure all data values are accounted for in the total row of the frequency distribution Avoid empty classes. It is undesirable for a histogram to display a class so narrow that there are no observations in it Avoid open-ended classes (if possible). These violate the first rule of equal class sizes Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Histogram with Grouped Quantitative Data For grouped data, the bins in Excel are the upper boundary for each class For continuous data, remove the gaps between the bars in the histogram: Right-click on any histogram bar to get a pop-up menu Left-click on Format Data Series In the dialog box, move the Gap Width slide all the way to the left Close the Format Data Series dialog box Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Histogram with Grouped Quantitative Data Additional formatting issues: Use a descriptive title for the graph Use descriptive labels for the axes Remove the redundant “Frequency” legend Remove gaps between bars Copyright ©2015 Pearson Education, Inc. 2-* The Consequences of Too Few or Too Many Classes Wide classes results in few class intervals Can obscure important patterns Gives a “blocky” distribution graph Summarizes the data too much Tells us little about the true distribution shape Too many narrow classes in a histogram also has consequences Results in a “jagged” histogram Some classes may be empty Does not summarize the data enough Copyright ©2015 Pearson Education, Inc. 2-* Are They Discrete or Continuous Data? Some data are technically discrete (counted, not measured) but are displayed in a continuous format Examples Age Income Other discrete data sets containing a wide range of values Copyright ©2015 Pearson Education, Inc. 2-* The Polygon A percentage polygon graphs the midpoint of each class as a line rather than a column The height of each midpoint represents the relative frequency of the corresponding class Used to compare the shape of two or more distributions on one graph The cumulative percentage polygon, or ogive, is a line graph that plots the cumulative relative frequency distribution Copyright ©2015 Pearson Education, Inc. 2-* The Polygon Percentage polygons and cumulative percentage polygons can be created using PHStat Copyright ©2015 Pearson Education, Inc. 2-* The Polygon Copyright ©2015 Pearson Education, Inc. 2-* 2.3 Displaying Qualitative Data Qualitative data are values that are categorical Can be nominal or ordinal measurement level Describe a characteristic, such as gender or level of education Frequency distributions help display qualitative data by indicating the number of occurrences of various categories Can use Excel’s COUNTIF function to count the number of values matching a category label Copyright ©2015 Pearson Education, Inc. 2-* Displaying Qualitative Data Figure 2.15 A-B | Excel’s COUNTIF Function Excel’s COUNTIF Function Results Copyright ©2015 Pearson Education, Inc. 2-* Bar Charts Bar charts are a good tool for displaying qualitative data that have been organized in categories Can be arranged in a vertical or horizontal orientation Copyright ©2015 Pearson Education, Inc. 2-* Bar Charts Horizontal bar chart Vertical bar chart Can display multiple series with clustered bar charts or stacked bar charts: Copyright ©2015 Pearson Education, Inc. 2-* Displaying Qualitative Data: Example Copyright ©2015 Pearson Education, Inc. 2-* Pareto Charts Pareto charts are bar charts that show the frequency of the categories that cause quality control problems Show quality problem categories in decreasing order The most problematic categories are shown first Pareto charts also plot the cumulative relative frequency as a line on the chart known as an ogive Copyright ©2015 Pearson Education, Inc. 2-* Pareto Charts Note: The categories are arranged from most frequent to least frequent Follow the steps shown in the text, pages 49-50, to create a Pareto chart and ogive using Excel Copyright ©2015 Pearson Education, Inc. 2-* Pie Charts Pie charts are another excellent tool for comparing proportions for categorical data Each segment of the pie represents the relative frequency of one category All categories in the data set must be included in the pie Use a pie chart to compare the relative sizes of all possible categories Bar charts are more useful when you want to highlight the actual data values and when the classes combined don’t form a whole Copyright ©2015 Pearson Education, Inc. 2-* Pie Charts Constructing a Pie Chart in Excel Figure 2.19A | Copyright ©2015 Pearson Education, Inc. 2-* Pie Charts Constructing a Pie Chart in Excel (continued) Figure 2.19B | Copyright ©2015 Pearson Education, Inc. 2-* Pie Charts Example: Copyright ©2015 Pearson Education, Inc. 2-* 2.4 Contingency Tables Contingency tables provide a format to display observations that have more than one value associated with them Use rows and columns for separate variables to summarize the data efficiently Copyright ©2015 Pearson Education, Inc. 2-* Contingency Tables 7 females out of 20 customers paid using credit, 7/20 = 0.35 Contingency Table Relative Contingency Table Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Contingency Table in Excel Click on any cell within your data Choose the Insert tab Click on the Pivot Table icon Click on Pivot Table in the drop-down menu A Create Pivot Table dialog box will appear. Click OK… Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Contingency Table in Excel A new worksheet will be created for your pivot table From the Pivot Table Field List, c. Drag the variable name to be summarized down into the Values box a & b. Drag the desired variable names down into the Column or Row Labels boxes, as desired a b c Copyright ©2015 Pearson Education, Inc. 2-* Constructing a Contingency Table in Excel Resulting pivot table: Creating a Pivot Table in Excel (Final Result) Figure 2.21C | Copyright ©2015 Pearson Education, Inc. 2-* 2.5 Stem and Leaf Display A stem and leaf display splits the data values into stems (the larger place values) and leaves (the smaller place value) By listing all of the leaves to the right of each stem, we can graphically describe how the data are distributed All the original data points are visible on the display Easy to construct by hand Provides a histogram-like view of the distribution Copyright ©2015 Pearson Education, Inc. 2-* Stem and Leaf Display For this example, use the 10’s digit as the stem Use the 1’s digit as the leaf 7 | 8 8 | 0 Copyright ©2015 Pearson Education, Inc. 2-* Stem and Leaf Display Sort the data from lowest to highest Determine the unique stem values 7, 8, 9 are the different stem values in this example List the stems in a vertical column and then add the leaf values to the right of the appropriate stem, in ascending order 7 | 8 8 9 9 9 8 | 0 0 0 0 1 1 2 3 3 4 4 4 5 6 7 8 9 | 0 2 5 Copyright ©2015 Pearson Education, Inc. 2-* Stem and Leaf Display To get more detail the stems can be split in half 7(5) | 8 8 9 9 9 8(0) | 0 0 0 0 1 1 2 3 3 4 4 4 8(5) | 5 6 7 8 9(0) | 0 2 9(5) | 5 The stem labeled 7(5) stores all the scores between 75 and 79 The stem 8(0) stores all the scores between 80 and 84 Copyright ©2015 Pearson Education, Inc. 2-* 2.6 Scatter Plots Scatter plots provide a picture of the relationship between two data points that are paired together The dependent variable, which is placed on the vertical axis of the scatter plot, is influenced by changes in the independent variable, which is placed on the horizontal axis Copyright ©2015 Pearson Education, Inc. 2-* Scatter Plots Dependent variable (y-axis) Independent variable (x-axis) Copyright ©2015 Pearson Education, Inc. 2-* Scatter Plots Constructing a Scatter Plot in Excel Figure 2.25A | Copyright ©2015 Pearson Education, Inc. 2-* Line Charts A line chart is a scatter plot in which the data points in the scatter plot are connected with line segments Often used with time series data When graphing a time series the convention is to place the time data on the horizontal axis Copyright ©2015 Pearson Education, Inc. 2-* Line Charts Constructing a Line Chart in Excel Figure 2.26A | Copyright ©2015 Pearson Education, Inc. 2-* All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. k value data Minimum value data Maximum width class Estimated - =